The GLM-spectrum: A multilevel framework for spectrum analysis with covariate and confound modelling

Andrew J. Quinn; Lauren Z. Atkinson; Chetan Gohil; Oliver Kohl; Jemma Pitt; Catharina Zich; Anna C. Nobre; Mark W. Woolrich

doi:10.1162/imag_a_00082

The GLM-spectrum: A multilevel framework for spectrum analysis with covariate and confound modelling

Andrew J. Quinn^*, Lauren Z. Atkinson, Chetan Gohil, Oliver Kohl, Jemma Pitt, Catharina Zich, Anna C. Nobre, Mark W. Woolrich

^*Corresponding author for this work

Psychology

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Abstract

The frequency spectrum is a central method for representing the dynamics within electrophysiological data. Some widely used spectrum estimators make use of averaging across time segments to reduce noise in the final spectrum. The core of this approach has not changed substantially since the 1960s, though many advances in the field of regression modelling and statistics have been made during this time. Here, we propose a new approach, the General Linear Model (GLM) Spectrum, which reframes time averaged spectral estimation as multiple regression. This brings several benefits, including the ability to do confound modelling, hierarchical modelling, and significance testing via non-parametric statistics. We apply the approach to a dataset of EEG recordings of participants who alternate between eyes-open and eyes-closed resting state. The GLM-Spectrum can model both conditions, quantify their differences, and perform denoising through confound regression in a single step. This application is scaled up from a single channel to a whole head recording and, finally, applied to quantify age differences across a large group-level dataset. We show that the GLM-Spectrum lends itself to rigorous modelling of within- and between-subject contrasts as well as their interactions, and that the use of model-projected spectra provides an intuitive visualisation. The GLM-Spectrum is a flexible framework for robust multilevel analysis of power spectra, with adaptive covariate and confound modelling.

Original language	English
Pages (from-to)	1-26
Number of pages	26
Journal	Imaging Neuroscience
Volume	2
Early online date	16 Jan 2024
DOIs	https://doi.org/10.1162/imag_a_00082
Publication status	Published - 2 Feb 2024

Bibliographical note

Funding & Acknowledgments:
This project was supported by the Medical Research Council (RG94383/RG89702) and by the NIHR Oxford Health Biomedical Research Centre. The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z). A.C.N. is supported by the Wellcome Trust (104571/Z/14/Z) and James S. McDonnell Foundation (220020448). M.W.W. is supported by the Wellcome Trust (106183/Z/14/Z; 215573/Z/19/Z). A.C.N. and M.W.W. are further supported by an EU European Training Network grant (euSSN; 860563). This research was funded in whole, or in part, by the Wellcome Trust. For the purpose of open access, the author has applied a CC-BY public copyright licence to any Author Accepted Manuscript version arising from this submission. The computations described in this paper were performed using the University of Birmingham’s BlueBEAR HPC service, which provides a High Performance Computing service to the University’s research community. See http://www.birmingham.ac.uk/bear for more details.

Keywords

Electroencephalography
regression
general linear model
neuronal oscillations
spectra
statistics

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The GLM-Spectrum: a multilevel framework for spectrum analysis with covariate and confound modelling
Quinn, A. J., Atkinson, L. Z., Gohil, C., Kohl, O., Pitt, J., Zich, C., Nobre, A. C. & Woolrich, M. W., 14 Nov 2022, bioRxiv.
Research output: Working paper/Preprint › Preprint

Cite this

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title = "The GLM-spectrum: A multilevel framework for spectrum analysis with covariate and confound modelling",

abstract = "The frequency spectrum is a central method for representing the dynamics within electrophysiological data. Some widely used spectrum estimators make use of averaging across time segments to reduce noise in the final spectrum. The core of this approach has not changed substantially since the 1960s, though many advances in the field of regression modelling and statistics have been made during this time. Here, we propose a new approach, the General Linear Model (GLM) Spectrum, which reframes time averaged spectral estimation as multiple regression. This brings several benefits, including the ability to do confound modelling, hierarchical modelling, and significance testing via non-parametric statistics. We apply the approach to a dataset of EEG recordings of participants who alternate between eyes-open and eyes-closed resting state. The GLM-Spectrum can model both conditions, quantify their differences, and perform denoising through confound regression in a single step. This application is scaled up from a single channel to a whole head recording and, finally, applied to quantify age differences across a large group-level dataset. We show that the GLM-Spectrum lends itself to rigorous modelling of within- and between-subject contrasts as well as their interactions, and that the use of model-projected spectra provides an intuitive visualisation. The GLM-Spectrum is a flexible framework for robust multilevel analysis of power spectra, with adaptive covariate and confound modelling.",

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author = "Quinn, {Andrew J.} and Atkinson, {Lauren Z.} and Chetan Gohil and Oliver Kohl and Jemma Pitt and Catharina Zich and Nobre, {Anna C.} and Woolrich, {Mark W.}",

note = "Funding & Acknowledgments: This project was supported by the Medical Research Council (RG94383/RG89702) and by the NIHR Oxford Health Biomedical Research Centre. The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z). A.C.N. is supported by the Wellcome Trust (104571/Z/14/Z) and James S. McDonnell Foundation (220020448). M.W.W. is supported by the Wellcome Trust (106183/Z/14/Z; 215573/Z/19/Z). A.C.N. and M.W.W. are further supported by an EU European Training Network grant (euSSN; 860563). This research was funded in whole, or in part, by the Wellcome Trust. For the purpose of open access, the author has applied a CC-BY public copyright licence to any Author Accepted Manuscript version arising from this submission. The computations described in this paper were performed using the University of Birmingham{\textquoteright}s BlueBEAR HPC service, which provides a High Performance Computing service to the University{\textquoteright}s research community. See http://www.birmingham.ac.uk/bear for more details.",

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AU - Gohil, Chetan

AU - Kohl, Oliver

AU - Pitt, Jemma

AU - Zich, Catharina

AU - Nobre, Anna C.

AU - Woolrich, Mark W.

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N2 - The frequency spectrum is a central method for representing the dynamics within electrophysiological data. Some widely used spectrum estimators make use of averaging across time segments to reduce noise in the final spectrum. The core of this approach has not changed substantially since the 1960s, though many advances in the field of regression modelling and statistics have been made during this time. Here, we propose a new approach, the General Linear Model (GLM) Spectrum, which reframes time averaged spectral estimation as multiple regression. This brings several benefits, including the ability to do confound modelling, hierarchical modelling, and significance testing via non-parametric statistics. We apply the approach to a dataset of EEG recordings of participants who alternate between eyes-open and eyes-closed resting state. The GLM-Spectrum can model both conditions, quantify their differences, and perform denoising through confound regression in a single step. This application is scaled up from a single channel to a whole head recording and, finally, applied to quantify age differences across a large group-level dataset. We show that the GLM-Spectrum lends itself to rigorous modelling of within- and between-subject contrasts as well as their interactions, and that the use of model-projected spectra provides an intuitive visualisation. The GLM-Spectrum is a flexible framework for robust multilevel analysis of power spectra, with adaptive covariate and confound modelling.

AB - The frequency spectrum is a central method for representing the dynamics within electrophysiological data. Some widely used spectrum estimators make use of averaging across time segments to reduce noise in the final spectrum. The core of this approach has not changed substantially since the 1960s, though many advances in the field of regression modelling and statistics have been made during this time. Here, we propose a new approach, the General Linear Model (GLM) Spectrum, which reframes time averaged spectral estimation as multiple regression. This brings several benefits, including the ability to do confound modelling, hierarchical modelling, and significance testing via non-parametric statistics. We apply the approach to a dataset of EEG recordings of participants who alternate between eyes-open and eyes-closed resting state. The GLM-Spectrum can model both conditions, quantify their differences, and perform denoising through confound regression in a single step. This application is scaled up from a single channel to a whole head recording and, finally, applied to quantify age differences across a large group-level dataset. We show that the GLM-Spectrum lends itself to rigorous modelling of within- and between-subject contrasts as well as their interactions, and that the use of model-projected spectra provides an intuitive visualisation. The GLM-Spectrum is a flexible framework for robust multilevel analysis of power spectra, with adaptive covariate and confound modelling.

KW - Electroencephalography

KW - regression

KW - general linear model

KW - neuronal oscillations

KW - spectra

KW - statistics

U2 - 10.1162/imag_a_00082

DO - 10.1162/imag_a_00082

M3 - Article

SN - 2837-6056

VL - 2

SP - 1

EP - 26

JO - Imaging Neuroscience

JF - Imaging Neuroscience

ER -

The GLM-spectrum: A multilevel framework for spectrum analysis with covariate and confound modelling

Abstract

Bibliographical note

Keywords

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The GLM-Spectrum: a multilevel framework for spectrum analysis with covariate and confound modelling

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